6533b858fe1ef96bd12b65f1

RESEARCH PRODUCT

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions

Christoph HelmaDenis GebeleDavid VorgrimmlerMartin GütleinJürg A. ZarnElena Lo PiparoBenoît SchilterBarbara E. Engeli

subject

0301 basic medicinePharmacologyTraining setlazarbusiness.industrylcsh:RM1-950Pattern recognition010501 environmental sciences01 natural sciencesexperimental variability(Q)SAR03 medical and health sciences030104 developmental biologylcsh:Therapeutics. PharmacologySimilarity (network science)Pharmacology (medical)Artificial intelligencebusinessChronic toxicityLOAEL0105 earth and related environmental sciencesApplicability domainMathematicsread-across

description

This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

10.3389/fphar.2018.00413http://journal.frontiersin.org/article/10.3389/fphar.2018.00413/full